机构：[1]Department of Radiation Oncology, Stanford University School of Medicine, Stanford, CA.[2]Department of Radiation Oncology, Wake Forest University School of Medicine, Winston-Salem, NC.[3]Department of Pathology, The Affiliated Yantai Yuhuangding Hospital of Qingdao University, Yantai, China.[4]Department of Pathology, Stanford University School of Medicine, Stanford, CA.[5]Department of Gastric Surgery, Sun Yat-sen University Cancer Center, Guangzhou, China.[6]Department of Pathology, West China Hospital, Sichuan University, Chengdu, China.四川大学华西医院[7]Else Kroener Fresenius Center for Digital Health, Technical University Dresden, Dresden, Germany.[8]College of Biomedical Engineering, Sichuan University, Chengdu, China.[9]Department of Biomedical Informatics, Harvard Medical School, Boston, MA.[10]Faculty of Medicine and Health, The University of Sydney, Camperdown, NSW, Australia.[11]School of Medical Sciences, University of New South Wales, Sydney, Kensington, NSW, Australia.[12]School of Population Health, University of New South Wales, Sydney, Australia.[13]School of Clinical Medicine, Tsinghua University, Beijing Tsinghua Changgung Hospital, Beijing, China.[14]Department of General Surgery, Nanfang Hospital, Southern Medical University, Guangzhou, China.[15]Stanford Institute for Human-Centered Artificial Intelligence, Stanford, CA.
Artificial intelligence (AI) holds significant promise for improving cancer diagnosis and treatment. Here, we present a foundation AI model for prognosis prediction on the basis of standard hematoxylin and eosin-stained histopathology slides.In this multinational cohort study, we developed AI models to predict prognosis from histopathology images of patients with GI cancers. First, we trained a foundation model using over 130 million patches from 104,876 whole-slide images on the basis of self-supervised learning. Second, we fine-tuned deep learning models for predicting survival outcomes and validated them across seven cohorts, including 1,619 patients with gastric and esophageal cancers and 2,594 patients with colorectal cancer. We further assessed the model for predicting survival benefit from adjuvant chemotherapy.The AI models predicted disease-free survival and disease-specific survival with a concordance index of 0.726-0.797 for gastric cancer and 0.714-0.757 for colorectal cancer in the validation cohorts. The models stratified patients into high-risk and low-risk groups, with 5-year survival rates of 49%-52% versus 76%-92% in gastric cancer and 43%-72% versus 81%-98% in colorectal cancer. In multivariable analysis, the AI risk scores remained an independent prognostic factor after adjusting for clinicopathologic variables. Compared with stage alone, an integrated model consisting of stage and image information improved prognosis prediction across all validation cohorts. Finally, adjuvant chemotherapy was associated with improved survival in the high-risk group but not in the low-risk group (treatment-model interaction P = .01 and .006) for stage II/III gastric and colorectal cancer, respectively.The pathology foundation model can accurately predict survival outcomes and complement clinicopathologic factors in GI cancers. Pending prospective validation, it may be used to improve risk stratification and inform personalized adjuvant therapy.
语种：
外文
PubmedID：
中科院(CAS)分区：
出版当年[2025]版：
大类|1 区医学
小类|1 区肿瘤学
最新[2025]版：
大类|1 区医学
小类|1 区肿瘤学
第一作者：
第一作者机构：[1]Department of Radiation Oncology, Stanford University School of Medicine, Stanford, CA.
共同第一作者：
通讯作者：
通讯机构：[1]Department of Radiation Oncology, Stanford University School of Medicine, Stanford, CA.[13]School of Clinical Medicine, Tsinghua University, Beijing Tsinghua Changgung Hospital, Beijing, China.[14]Department of General Surgery, Nanfang Hospital, Southern Medical University, Guangzhou, China.[15]Stanford Institute for Human-Centered Artificial Intelligence, Stanford, CA.
推荐引用方式(GB/T 7714)：
Wang Xiyue,Jiang Yuming,Yang Sen,et al.Foundation Model for Predicting Prognosis and Adjuvant Therapy Benefit From Digital Pathology in GI Cancers[J].Journal Of Clinical Oncology : Official Journal Of The American Society Of Clinical Oncology.2025,JCO2401501.doi:10.1200/JCO-24-01501.
APA：
Wang Xiyue,Jiang Yuming,Yang Sen,Wang Fang,Zhang Xiaoming...&Li Ruijiang.(2025).Foundation Model for Predicting Prognosis and Adjuvant Therapy Benefit From Digital Pathology in GI Cancers.Journal Of Clinical Oncology : Official Journal Of The American Society Of Clinical Oncology,,
MLA：
Wang Xiyue,et al."Foundation Model for Predicting Prognosis and Adjuvant Therapy Benefit From Digital Pathology in GI Cancers".Journal Of Clinical Oncology : Official Journal Of The American Society Of Clinical Oncology .(2025):JCO2401501
医学